基于Haar小波特征的恒星光谱物理参量自动估计

恒星大气物理参数的自动测量是大型巡天计划中海量光谱数据自动处理中的一个重要内容。首先使用多尺度Harr小波对恒星光谱数据进行特征分解，然后选用相应的小波系数作为光谱的特征向量，最后采用非参数回归算法对光谱的物理参数进行估计。研究表明，只需对光谱进行四层小波分解, 并选择第四层小波系数作为光谱的特征向量，即可获得重力加速度和表面有效温度的较好估计。对于化学丰度的估计，选择第一层小波系数作为光谱特征向量可取得较好效果。选用文献相关研究中常用的恒星大气模拟模型合成光谱库ELODIE中光谱数据测试了该方法的有效性。结果表明，基于Harr小波分解的光谱特征提取方法对恒星表面温度、表面重力和化学丰度等物理参数的估计具有较高的精度和鲁棒性。

Automatic Measurement of Physical Parameters of Stellar Spectra Based on the Haar Wavelet Features

The present paper researches the automatic measurement of the physical parameters ofthe stellar spectra. It is an important problem of the automatic processing of mass spectral data in the large-scale survey plan. The basic steps of the program in this article are: at first, the stellar spectra are decomposed by multi-scale Harr wavelet. Secondly, wavelet coefficients are chosen as the feature vectors of the spectrum. Finally, Non-parameter estimation is employed for estimating physical parameters of the stellar spectra. Studies show that the original spectrumonly needs to be decomposed by four-level Harr wavelet. If the wavelet coefficient at the fourth level is chosen as the wavelet feature of the spectrum, the surface gravity and effective temperature is estimated better. If the wavelet coefficient at the first level is chosen as the wavelet feature of the spectrum, the metallic abundance is estimated better. The authors use the spectral data in the literature ELODIE library to test the effectiveness of the method. When the wavelet coefficient is chosen as the feature vector of the spectrum, the experiment results show that the proposed method is robust and features high accuracy for the automatic measurement of the surface gravity, the effective temperature and the metallic abundance.